From glass boxes to black boxes

I wrote about how platforms are glass box organisations, given their network effects. But there are a lot of black boxes out there. In the digital platform space, there are a lot of firms that operate as black boxes. The glass box metaphor was heavily drawn on the internal culture of the firm, that is, what the employees of the firms do. This post is about what the employees don’t do.

Before I elaborate, let me highlight the work of one of my doctoral students, Sandeep Lakshmipathy. He elucidates four primary value creation opportunities of multi-sided platforms: Discovery, matching, transaction, and evaluation. In short, discovery platforms help reduce the search costs for the sides of the platform (think Craigslist), matching platforms use filters and algorithms to ensure that the preferences of both sides are catered to while delivering a match (think Tinder), transaction platforms reduce the frictions and transaction costs in interacting with the other sides of the platform (think MasterCard), and evaluation platform enable ratings/ reviews/ recommendations/ feedback of the service (think Yelp). Sure, some platforms provide multiple value, like Uber provides discovery, transaction, and evaluation; whereas Airbnb provides all four.

Our focus today is just matching platforms, and how they create more and more opaque black boxes.

Matching platforms

In one of our conversations about the difference between discovery and matching platforms, Sandeep quipped about the difference between choosing a tomato sauce on an e-commerce platform and choosing a partner on Tinder. While it is sufficient for me to like the tomato sauce, it is not important for the tomato sauce to like me! Unlike this, in a matching platform like Tinder, it is imperative for both sides to have liked each other. Here is where the algorithms kick in. Magic! Matching algorithms.

Matching algorithms are technically taught in graph theory. Graph theorists discuss two types of matching – common vertex matching and bipartite matching. Common vertex matching is used to match, for instance, people with similar interests, like students in a class interested in a specific project. On the other hand, bipartite matching is used to match two subsets with each other, like buyers and sellers in an e-commerce platform. There are various algorithms used by graph theorists, including Hungarian maximum matching algorithm, Edmond’s matching algorithm, and the Hopcroft-Karp algorithm. The specificity of the algorithms notwithstanding, each of these algorithms work on the basis of three things – the specific preferences as limiting criteria, the minimum matches to be returned, and the maximum matches possible.

Imagine when you search for books to read on a peer-to-peer book-reading platform, and based on your preferences, you get exactly one recommendation. Just not sufficient enough choice, right? On the other hand, irrespective of the filters you add, if the recommendations do not change (the same titles keep appearing) and you get a recommendation of 5632 books, you feel overwhelmed. Both sides of the matching algorithms, there is a problem – of underwhelming and overwhelming choice. It is exactly to solve these problems that matching algorithms collect enough data from the users.

Some algorithms start with providing random matches, and based on the expressed user preferences, gradually mature their matching. Some others start at one extreme – like the shopping assistant in a mom-n-pop retail store. Once she’s estimated your broad preferences and budgets, she is most likely to start with showing you options very close to your budget. Pretty much like the default sorting algorithm (lowest price first) in the case of travel/ hotel aggregation platforms. Again, the platforms “learn” based on your expressed preferences.

Expressed preferences to profiling

Now, how do these platforms learn, and what do they learn about your preferences is the black box. Based on a few expressed preferences, these matching algorithms may end up profiling the user, and start providing more and more matches based on that specific profile. Sometimes, these preferences may be specific to a context – like me searching for a business class air ticket (when the client has agreed to pay – I travel economy otherwise!). The algorithm has no way of separating out such preferences without access to a large number and variety of such searches. And such preference-based profiling is easy.

Have you wondered why you get “more relevant” advertisements on a Google search results page than on YouTube? Both owned by the same firm, and possibly can share the matching algorithms. However, the way one could profile search users based on their text inputs and matching them with appropriate websites and advertisements is far easier than in the context of video content. Video content may be devoid of explicit tags that are indexed, may contain sarcasm, the audio and video content may be inadequate, or just non-existent. So, there are days I have been left wondering why I was exhibited a particular advertisement in the beginning of a YouTube video. Sometimes, I conjure up my own hypothesis about what actions in my history, the cookies on my browser/ device (my kids do use my iPad), and what specific search terms triggered those. Black box!

Customization-personalization or privacy: A trade-off

This is no simple trade-off: the one between customization and personalization and privacy. By exposing my preference for a particular sport (like cricket) to YouTube, I get to see a lot of interesting cricket video suggestions, right up front on the home page. By subscribing to specific channels, liking certain content, and commenting on some others, I help YouTube learn more about me. These actions provide enough inputs for the platform to customize the match and personalize suggestions. However, there are limits to when such expressed preferences breach privacy. Like when my phone’s AI assistant suggests a wake-up time for me based on my first appointment of the day, or when my wearable device chides for not walking enough during the day (how many steps can you walk in days like today, when the entire country is under lockdown?).

This is not new; and it could be creepy. Remember the 2012 story about the US retailer Target sending out mailers about baby care to a teenage girl, and the parent discovering it? Neither the local Target store had a clue, nor did the parent. The Target central database was able to predict something as personal as teenage pregnancy! Target realized that they were spooking people, and would randomize the offers, like putting ads for lawn mowers next to diaper coupons. But still, they knew!

Dealing with black boxes

I wish I had a set of recommendations for you! One day, possibly. But today, this is just a personal trade-off. As for me, I wouldn’t mind sharing my location data with my phone as long as it provides me good navigation services. And my food delivery app to know where I am, so that I get choices from hyper-local restaurants. I would go one step further and allow my photos app to have my location access so that I could organize my photos by date and location. But to allow location access to online newspapers, no.

Happy matching.

Stay home, stay safe, stay healthy.

© 2020. Srinivasan R

Glass box organizations: Platforms

Way back in September 2017, David Mattin of Trend-Watching wrote about Glass box brands. He argued that organizations are moving away from being black boxes (where customers could only see what was painted outside) to glass boxes, where everything that happens inside and outside of the organization is visible to everyone.

The primary arguments of the glass box world are: (a) in an era of social media and high organizational attrition, even the mundane activities like routines and rituals are visible to the outside world; and (b) trends like automation, inequality, and globalization have led to “meaningful consumerism”, bordering on activism. Therefore, consumers are making choices about their brand affiliation and loyalty based on the company culture and values, apart from other considerations.

If the internal culture is the window of the brand to the outside world, it is important for every organization to meaningfully nurture it, articulate it, and live it. I am not going to dwell on how to develop your internal culture and values, but the implications of the glass box metaphor in the context of platforms and digital organizations.

Multi-sided platforms as glass boxes

By definition, multi-sided platforms (MSPs) have many “sides” that drive network effects. For instance, a guest chooses to use Airbnb while travelling because she values the number and quality of hosts. When Airbnb doesn’t treat one side well, it directly impacts the quality of interaction with the other side and affects the strength of network effects. Which in turn, affects the willingness to join (WTJ) and willingness to pay (WTP) of the users on the other side. The quality of the platform deteriorates and can even degenerate into a “market for lemons”. Such dynamics of network effects ensure that platforms do not unduly favor one side over the other, especially when there are cross-side network effects. However, these do not include how the firm treats its employees – remember Travis Kalanick and Uber?!

Digitalization and glass boxes

The omnipresent social media and the constant need by employees and customers to document share their experiences online (most often with the general public, including strangers) has been one of the drivers of glass walling of organizations. Isn’t it why the digital platform that allows for employees to review their workplaces called glassdoor.com? Sure, glassdoor.com monetizes its corporate side of the network through its recruitment services, but its primary differentiator is the large volume of anonymous employee reviews of the work culture and salary structures. We know that when the side that is being reviewed is monetized, it is in the interest of the firm to have good quality reviews on the platform, failing which it finds it difficult to attract enough quality candidates. There is enough incentive to witch hunt people who write bad reviews, as well fill the site with paid/ fake reviews to overshadow the “real” bad reviews. It is still a glass door after all, not a glass box!

Digitalization of employee experience holds a significant potential in managing the quality of the brand, as perceived outside the firm. A lot of firms focus on improving customer experience in their digitalization journeys, but employee experience is equally critical (read more about it in one of my earlier posts). Good employee experience ensure that the positive experiences have spill-over effects on efficiency, performance, internal culture, as well as customer experience. A variety of organizations including VMWare, SAP and IBM have laid explicit focus on improving employee experience in their digital transformation journeys.

Stay home, stay safe, stay healthy!

(c) 2020. Srinivasan R.

Remora Strategies

It is interesting how much management as a discipline borrows from other disciplines. Much like the English language. That is for another day. Today, working from home, I got reading a lot of marine biology. Yes, you heard it right, marine biology. It is about Remora fish, and its relationship with sharks and other larger marine animals. Students in my IIMB MBA class of 2020 have heard of it in one of my sessions in the platform business course and a couple of groups also used this concept in their live projects.

What is a Remora and what is its relationship with Sharks?

The Remora is a fish. It is possibly the world’s best hitchhiker. It has an organ that allows it to attach itself to a larger animal, like a shark or a whale. The sucker-like organ is a flat surface on its back, that allows itself to attach to the belly of a shark. That is a reason why it is also popularly known as a sharksucker or whalesucker. Remoras have also been known to attach themselves to divers and snorkelers as well. The sucker organ looks like venetian blinds that increase or decrease its suction on the larger fish’s body as it slides backward or forward. They could therefore be removed by sliding them forward.

Remoras can swim on their own. They are perfectly capable. But they prefer to attach themselves to the larger fish to hitch a ride to deeper parts of the ocean, saving precious energy. Their relationship with the Shark is unique – they do not draw blood or nutrients from the Shark like a Leech. They feed on the food scraps of the larger fish by keeping their mouths open. While the Remora benefits from its attaching to the Shark, it does not significantly benefit or harm the Shark. Some scientists argue that Sharks like the fact that Remoras feed on the parasites that are attaching themselves to the skin of the Shark and thereby keeping them healthy. Some others are concerned about the drag experienced by Sharks as they swim deeper in the oceans, which can be significant when there are dozens of Remoras attached to the Shark. Both of these are not that significant enough for the Sharks to either welcome Remoras to attach themselves to their bellies, nor have they exhibited any behaviour to repel these Remoras away (like they do with other parasites). Read more about Remoras’ relationship with Sharks here.

This relationship between the Remora and the shark can be termed as commensalism, rather than symbiotic. If the Sharks indeed value the fact that Remoras can help them get rid of the parasites from their teeth or skin, then we could term this relationship as mutualistic.

Remoras and platform start-ups

What is a platform researcher studying Remoras? A platform start-up could solve its Penguin problem using a Remora strategy. It could piggy-back on a larger platform to access its initial set of users, with no costs to the larger platform. Let’s consider an example. A dating start-up struggles to get its first set of users. While it needs rapid growth of numbers, it should ensure that the profiles on the platform are of good quality (bots, anyone?). It has two options: developing its own validation algorithm or integrating with larger platforms like Twitter or Facebook for profile validation. It could create its own algorithms if it needs to validate specific criteria, though. It could use a Remora strategy, by attaching itself to a larger Shark in the form of Twitter or Facebook. This has no costs to Twitter or Facebook, and if at all, contributes to marginal addition of traffic to Facebook/ Twitter. However, for the start-up, this saves significant costs of swimming down the depths of the ocean (developing and testing its own user validation algorithms).

Remora’s choice

Don Dodge first wrote about the Remora Business Model, where he wondered how both the Remoras and the Sharks made money, if at all. Building on this, Joni Salminen elaborated on Remora’s curse. Joni’s dissertation elaborates two dilemmas multi-sided platforms face – cold-start and lonely-user.

The cold-start dilemma occurs when a platform dependent on user-generated content does not get sufficient enough content in the early days to attract more users (to consume and/ or generate content). There are two issues to be resolved in this case – to attract more users to sustain the platform, and in the process balancing the numbers of content generators and content consumers.

The lonely-user dilemma occurs when a platform dependent on cross- and same-side network effects tries to attract the first users. A subset of the penguin problem, on this platform nobody joins unless everybody joins. There is no intrinsic value being provided by the platform, except that being generated by interactions between and among user groups.

The cold-start dilemma can be typically resolved using intelligent pricing mechanisms, like subsidies for early adopters. For example, a blogging platform can attract influencers to start blogging on their site, by providing them with premium services. As they resolve the cold-start dilemma, and they attract enough users to blog and read (generate and consume), they could get to a freemium model (monetize reading more than a specified number of posts), while continuing to subsidising writers. The key is to identify after what number of posts, does one start charging readers, as too low a number would reduce the number of readers and high-quality writers would leave the platform; but on the other hand, too big a number of freely available posts to read, the platform may not make any money at all to sustain.

The lonely-user dilemma can be typically resolved by following a Remora strategy. By leveraging the users on a larger established platform, the first set of users could be sourced easily en masse. However, just having users is not sufficient – there is an issue of coordination: getting not just sign-ups but driving engagement. It is important that registered users begin engaging with the platform. Some platforms need more than just engagement, they are stuck with a real-time problem: like in  a multi-player gaming or a food-delivery platform, we need gamers to be engaged with each other real-time. Some other platforms need users in specific segments, or the transferability problem: that users are looking for others within a specific segment, like in a hyperlocal delivery platform, a matrimony platform or a doctor-finding platform. Such platforms need to have sufficient users in each of these micro-segments.

A Remora strategy could potentially help a platform start-up overcome these two major dilemmas – cold-start and lonely-user. By porting users from the larger platform, one could solve the lonely-user problem, and through tight integration with the content/ algorithms of the Shark platform, the Remora (start-up) could manage the cold-start problem.

Remora’s curse

The decision to adopt a Remora strategy is not just simple for a platform start-up. There may be significant costs in the form of trade-offs. I could think of five significant costs that need to be considered along with the benefits of following a Remora strategy. These costs include (a) holdup risk; (b) ceding monetization control; (c) access to user data; (d) risk of brand commoditization; and (e) exit costs.

Hold-up risk: There is a significant risk of the established platform holding the start-up to a ransom, partly arising out of the start-up making significant asset-specific investments to integrate. For instance, the dating start-up would need to tightly integrate its user validation processes with that of Facebook or Twitter, as the need may be. It may have to live with the kind of data Facebook provides it through its APIs. It may be prone to opportunistic behaviour by Facebook, when it decides to change certain parameters. For example, Facebook may stop collecting marital status on its platform, which may be a key data point for the dating start-up. Another instance of hold-up risk could be when Google resets its search algorithm to only include local search, rather than global search, thereby affecting start-ups integrating with Google.

In order to manage hold-up risks, Remora start-ups will be better off not making asset-specific investments to integrate with the Shark platforms.

Monetization control: A significant risk faced by Remora start-ups is that of conceding the power to monetize to the Shark. For example, when a hyper-local restaurant discovery start-up follows a Remora strategy on Google, it is possible that Google gets all the high-value advertisements, leaving the discovery start-up with only low-value local advertisements. There is also a risk of the larger platform defining what could be monetised on the start-up platform as well. For example, given that users have gotten used to search for free, even specialised search like locations (on maps) or specialised services like emergency veterinary care during off-working hours, may not be easy to monetise. Such platforms may have to cede control on which side to monetise and subsidise, and how much to price to the larger platform.

To avoid conceding monetization control to larger platforms, Remora start-ups need to provide additional value over and above the larger platform. For instance, in the local search business, a platform start-up would possibly need to not just provide discovery value (which may not be monetizable) but include matching value as well.

Access to user data: This is, in my opinion, the biggest risk of following a Remora strategy. Given that user data is the primary lever around which digital businesses customize and personalize their services and products, it is imperative that the start-up has access to its user data. It is likely that the larger platform may restrict access to specific user data, which may be very valuable to the start-up. For instance, restaurant chains who could have run their own loyalty programmes for its clients, may adopt a Remora on top of food delivery platforms like Swiggy or Zomato. When they do that, the larger platform may run a loyalty programme to its clients, based on the data it has about the specific user, which is qualitatively superior to the one that local restaurants may have. In fact, in the context of India, these delivery platforms do not even pass on basic user profiles like demographics or addresses to the restaurants. The restaurants are left with their limited understanding of their walk-in customers and a set of nameless/ faceless customers in the form of a platform user, for whom they can generate no meaningful insights or even consumption patterns.

It is imperative that platform start-ups define what data they require to run their business model meaningfully, including user data or even operations. It could be in the form of specific contracts for accessing data and insights, and/ or co-creating analytical models.

Risk of brand commoditization: A direct corollary of the user data is that the Remora start-up could be commoditized, and their brand value might be subservient to the larger platform’s brand. It could end up being a sub-brand of the larger start-up. For user generation and network mobilization, the Remora start-up would possibly need to get all its potential users to affiliate with the larger platform, even if may not be most desirable one. On a delivery start-up, hungry patrons may be loyal to the aggregator and the specific cuisine, rather than to a restaurant. Given that patrons could split their orders across multiple restaurants, it could be the quality and speed of delivery that matters more than other parameters. Restaurants might then degenerate into mere “kitchens” that have excess capacity, and when there is no such excess capacity, these aggregators have known to set up “while label” or “cloud kitchens”.

It is important that Remora start-ups step up their branding efforts and ensure that the larger brand does not overshadow their brand. The standard arguments or relative brand strengths of complements in user affiliation decisions need to be taken into consideration while protecting the Remora’s brands.

Exit costs: The last of the Remora’s costs is that of exit costs. Pretty much similar to the exit costs from an industry, platform start-ups need to be clear if their Remora strategy is something temporary for building up their user base and mobilizing their networks in the early stages, or it would be relatively permanent. In some cases, the platform’s core processes might be integrated with the larger platform, like the API integration for user validation, and therefore may provide significant exit costs. In some other cases, the platform may have focused on their core aspects of their business during the initial years and would have relegated their non-core but critical activities to the larger platform. At a time when the start-up is ready to exit the larger platform, it may require large investments in non-core activities, which may lead to disruptions and costs. Add to this, the costs of repurposing/ rebuilding asset-specific investments made when joining the platform.

Remora start-ups, therefore, need to have a clear strategy on what is the tenure of these Remora strategies, and at what point of time they would exit the association with the larger platform, including being prepared for the costs of exit.

Scaling at speed

Remora strategies allow for platform start-ups an alternative to scale their businesses very fast. However, it is imperative to understand the benefits and costs of such strategies and make conscious choices. These choices are at three levels – timing of Remora, what processes to Remora, and building the flexibility to exit. Some platforms may need to attach themselves right at the beginning of their inception to larger platforms to even get started; but some others can afford to wait for the first users to start engaging with the platform before integrating. What processes to integrate with the larger platform is another critical choice – much like an outsourcing decision, core and critical processes need to be owned by the start-up, while non-core non-critical processes may surely be kept out of the platform. In all of these decisions, platform start-ups need to consciously decide the tenure and extent of integration with the larger platform, and therefore make appropriate asset-specific investments.

Maintain social distance, leverage technology, and stay healthy!

Quote of the times

(C) 2020. Srinivasan R

Collaborative economy, sharing economy, gig economy … what are they?

If you have been reading any technology-business interface discussions recently, you must have surely heard of these (and more) words…. Almost all platform businesses have been described using one or more of these labels. In this post, I analyse their meanings and differences.

Collaborative economy

As the name suggests, collaborative economy is when different parties collaborate and create new, unique value that would not have been possible individually. For instance, economic activities like crowdfunding or meetup groups qualify as parts of the collaborative economy. Platforms like Kickstarter or Innocentive help people collaborate and create new value. However, platforms like Uber or Airbnb do not qualify – drivers (on Uber) and hosts (on Airbnb) do not collaborate amongst themselves or with their riders (on Uber) and guests (on Airbnb) – they do business with the other side.

Sharing economy

At best, Uber and Airbnb, in their purest forms qualify as sharing economy participants. In the sharing economy, participants share their surplus assets/ capacity with others (for a fee, of course); and there may be platforms facilitating this discovery and sharing (for a fee, surely). When this sharing is a capital asset like a house (in Airbnb) or a piece of high-value equipment (in Makerspaces), the economic argument is based on high fixed/ sunk costs and with low marginal costs, coupled with low capacity utilization/ spare capacity. This is when these economic transactions become sharing. For instance, when I am driving long distance alone, and want company for the distance (in the process, also sharing the cost), I could use Bla Bla Car, and that would be sharing economy, as the three conditions are met: (1) the asset shared (the car) is a capital asset; (2) the marginal cost of adding another passenger to the car is negligble; and (3) the car has space for the additional passenger. The value is created for both of us – I got company through the trip as well as reduced the cost; my co-passenger got to travel in a comfortable manner ‘with company’ at a much lower cost. I am not a professional driver, who is looking to make money by transporting passengers from city A to city B. That would make me a ‘gigzombie’.

The gig economy

Used almost in a derogatory manner, the gig economy refers to the idea of loosely connected people sharing their labor/ expertise for professional returns. These laborers are not employees, but are on real short-term jobs or ‘gigs’. These short-term gigs are provided by some platforms like Uber. What Uber has come to today is to create a marketplace for professional drivers. Uber does not employ them (with all the benefits and security of employment), but treats them so. Uber attracts professional driver-partners to serve their riders. The opportunity costs for these drivers are pretty high, unlike in the sharing economy. For instance, in the context of the Bla Bla Car, if I did not find a partner (or someone I liked), I would still drive that long distance, because I had work in that city. Co-passenger or not, I would still go. I am not dependent on Bla Bla Car for meeting my costs. What the a gig economy company like Uber does is to hire professional drivers (who would have otherwise been employed as drivers or in other roles) and give them business. This is fine as long as the ‘gig’ was a small proportion of your total work.

Take for instance a photographer, who has his own professional practice. He acquires his customers through direct sales, word-of-mouth, and search engine/ social media marketing. And when a gig economy platform like Urbanclap begins providing him business, it adds to his exisitng income. And he is willing to pay a commission to Urbanclap for getting him customers (which he would have otherwise found difficult to get). However, when Urbanclap provides him ‘all’ his business, the ‘gig’ economy kicks in, and the photographer is at the mercy of his one and only source of jobs. He is now a ‘gigzombie’, and the aggregator can steeply increase her commissions.

Point to note is that in the gig economy, there is no collaboration/ co-creation (no common value added), nor is there a shared-value creation (fixed assets, low marginal costs, and excess capacity).

Business models for collaborative, shared, and gig economies

Given that these three economies have different architectures of interaction amongst partners, we cannot have the same business models serving them. Collaborative economies require business models where the platform allows for parners to complement each others’ value creation efforts; sharing economies require business models to match excess capacity with demand; whereas gig economies require aggregation and improving efficiency of the overall system.

‘Bargaining’ power of gig economy platforms

Given that some of these gig economy platforms operate in winner-takes-all markets with high multi-homing costs, their barganing power (in the traditional sense of the term) increases significantly. Multi-homing costs refer to the bureaucratic (search and contracting) and transaction (including variable) costs incurred by a set of partners in maintaining affiliations with multiple platforms simultaneously (switching costs refer to the costs of abandoning one in favor of the other platform). For instance, given the high multi-homing costs for drivers on the Uber platform, it is nigh impossible for a driver-partner to fairly negotiate the terms of engagement with Uber. Combine this with the unorganized nature of the gigzombies, we have a potential for an organized exploitation.

Policy issues in gig economies

Gig economies typically attract ‘weak’ partners, at least on the one side. For instance, Uber drivers work like employees (full-time with Uber) with litle or no employment benefits. The imbalance is pretty stark when these platforms begin disrupting existing, established business models. Take the arguments against Airbnb around systematic reduction of supply of long-term housing in cities. When house-owners are faced with a choice of long-term rental contracts and short-term rentals with Airbnb, they may choose the more financially lucarative short-term Airbnb rentals; and when more and more home-owners do this in a city, the supply of housing reduces, driving up rental prices.

The impact of gig economy platforms on employment is further stark – especially when the labor is ‘online work’. In such cases, the worker in San Bernandino, California is competing with similarly trained workers in Berlin (Germany), Beijing (China), Bangalore (India), or Bangkok (Thailand). Obviously the labor costs are different, and lack of regulations favor significant displacement of work to cheaper options. Even in the case of physical labor like driving cars, developed countries have been dependent on countries with demographic dividend and lower costs of skilling (costs of education and vocational training) for immigration (some world leaders’ public stances notwithstanding).

Do not confuse the the three of them!

In summary, we are talking three different business models when we talk of collaborative, sharing and gig economies. And they need to be treated differently. Primarily, regulators and investors need to understand the differences and frame policies that are sensitive to these differences.

Cheers!

(c) 2018. R. Srinivasan

 

Predatory pricing in multi-sided platforms

Over the last few days, living and commuting in Nuremberg, I realised that I was not missing Uber. While just about a month ago, commuting for a week in suburban Paris, I was completely dependent on Uber for even the shortest of distances. The penetration of public transport and my familiarity with the city of Nuremberg aside, I began wondering how would Uber price its services in a city like Nuremberg (when it enters here, which I doubt very much would happen in the next few years), where public transport is omni-present, efficient, and affordable. It surely should adopt predatory pricing.

In this post, I will elaborate on the concept of predatory pricing in the context of multi-sided platforms.

Theory alert! If you are uncomfortable with theory, skip directly to the illustration and come back to read the theory.

What is predatory pricing?

Economists and policy makers concerned about market efficiencies and fair competition have been obsessed with the concept of predatory pricing for a long time. The most common definition of predatory pricing is through the application of the conventional Areeda-Turner test. Published way back in 1975, in spite of its limitations, most countries and courts have used it consistently, due to, in some ways, lack of any credible alternative.

The Areeda-Turner test is based on two basic premises. The recoupment premise states that the firm indulging in predatory pricing should be able to predict and be confident of its ability to recoup the losses through higher profits as competition exits the market. The assumption is that the firm could reasonably anticipate the (opportunity) costs of predatory pricing, as well as have an estimate of the future value of monopoly profits; and the net present value of such predatory pricing to push competition out of the market should be positive and attractive. In plain English, the firm should be able to project the effect of lower prices in terms of lower competition and higher profits in the future.

How low can this predatory price be? That is the subject of the second premise – the AVC premise. The firm’s prices (at business as usual volumes) should be below its average variable costs (AVC), or marginal costs in the short run. If the prices were indeed above the AVC, the firm would argue that they are indeed more efficient than competition, due to any of their resources, processes, or organisational arrangements. It is when the price falls below the AVC that the question of unfair competition arises – the firm might be subsidising its losses.

Take for instance, a start-up that is piloting an innovative technology. It may price its products/ services at a price below the AVC to gain valuable feedback from its lead users, but in the absence of a recoupment premise such pricing might not qualify as predatory pricing. On the other hand, imagine a new entrant with superior technology who can bring costs down to a level where the prices fall below the marginal costs of the competitors but stay well above the firm’s AVC, it is just disrupting the market.

Only when both the conditions are met, i.e., when the predator’s prices are below the AVC and the firm could project the extent of recoupment due to monopoly profits as competition exits the market, that we call it predatory pricing.

Predatory pricing in MSPs

There has been a lot of discussion about how ecommerce firms in India have been indulging in predatory pricing and how various platforms have been going under. I had written about subsidies and freebies from a consumer perspective a few months ago (Free… continue hoyenga). Let us discuss how and why it is difficult to assess if a lower-than-competition price is indeed predatory in the context of multi-sided platforms (MSPs).

  1. Multi-sided platforms have a unique problem to solve in their early days, that of network mobilisation. A situation that is like a chicken-egg problem, or a Penguin problem, where “nobody joins unless everyone joins” is prevalent in establishing a two-sided or multi-sided platform (for more details about the Penguin problem and network mobilisation strategies, read my earlier post here). In order to build a sufficient user base on one side, a common strategy is to subsidise, even provide the services free.
  2. Another common feature of MSPs is the existence of subsidy-sides and money-sides of users. The platform might subsidise one side of users and make money from the other side, while incurring costs of providing services to both sides, depending on the relative price elasticities and willingness to affiliate with the other side of the platform. And the prices for the subsidy side would surely below costs for that side. It is imperative that the overall costs and prices are considered while analysing these pricing strategies.
  3. These cross-side network effects will surely force the platforms to price their services most efficiently across both the sides. Even for the money side, the platform might not be able to charge extraordinary prices as such prices would themselves act against the sustenance of these cross-side network effects. It is likely that these extra-normal profits would evaporate through subsidies on the other side to keep the network effects active. Imagine a situation where a B2B marketplace charged the sellers higher than normal prices, only large (and desperate) sellers would affiliate with the marketplace, leading to buyers (the subsidy side) leaving the platform. In order to keep the buyers interested, the marketplace might either have to broaden the base of sellers by optimising the prices, or provide extraordinary subsidies to the buyers to keep them interested. So in order to maintain the equilibrium, the platform would have to price the sides efficiently.
  4. Finally, in a competitive situation, not all competitors might follow the same price structure. So, a reduction of prices by one competitor for one side of the market may not force all other competitors to reduce prices; they may just encourage multi-homing (allowing users to use competitive products simultaneously) or manipulate the price on the other side of users.

So, a direct application of the Areeda-Turner test might not be appropriate while studying predatory pricing in the context of MSPs.

An illustration

Let us imagine a market for home tutors supporting school students. The market is inherently geographically constrained; it is very unlikely that either the teacher or the student would travel across cities for this purpose. For the time being, let us assume that there is no technology (like video conferencing) being used.

This market is apt for the entry of a multi-sided platform, like LocalTutor. This firm provides a platform for the discovery and matching of freelance tutors with students. LocalTutor monetises the student side by charging a monthly fee (that includes a platform commission), and passes on the fees to the tutor. We need to make two assumptions before we proceed with competitive entry and predatory pricing: the market is fully penetrated (all the students who are looking for tutors and tutors looking for students are all in the market) and there are no new students and tutors entering the market; and there are no special preferences between student-tutor matches, i.e., the student-tutor pair does not form a bond like a sportsperson-coach, where they begin working like a team. In other words, the tutor is seamlessly (with no loss of efficiency) replaceable.

Now imagine a new competitor enters the market and engages in predatory pricing to kick-in network effects. The new entrant, let’s call it GlobalTutor (a fictitious name), drops the student-side prices to half. In order to attract the right number and quality of tutors, GlobalTutor has to sustain the same fees that LocalTutor provides its tutors, if not more. So, it starts dipping into its capital reserves and begins paying the tutors the market rates while reducing the student fees. Anticipating a larger surge in student numbers, more tutors sign up to GlobalTutor, and seeing the number and quality of tutors on GlobalTutor (at least if it is not inferior to LocalTutor), students first start multi-homing (use both services for their different needs, like LocalTutor for mathematics and GlobalTutor for music classes), and some of them begin switching.

In a fully penetrated market, the only way for LocalTutor to compete is to respond with its price structure. It has two options – reduce the student-side prices to restrain switching and multi-homing behaviour; and tweak the tutor-side prices and incentives. The first option is straightforward; it is cost-enhancing and profit-reducing. The second option (which is not available for pipeline businesses) is interesting in the context of platform businesses.

There are various ways of responding to this threat. The intent is to arrest switching and multi-homing behaviour of tutors and students from LocalTutor to GlobalTutor.

  1. Increasing multi-homing costs of tutors by providing them with incentives based on exclusivity/ volume: Like what Uber/ Ola provides its drivers – the incentives kick-in at what the company believes is the most a driver can do when they do not multi-home. In other words, if you multi-homed, drove your car with both Ola and Uber, you would never reach those volumes required to earn your incentives in either of your platforms.
  2. Contractual exclusion: This might not be tenable in most courts of law, if these freelance tutors were not your ‘employees’. Given the tone of most courts on Uber’s relations with its driver-partners (drivers’ lack of control in most of the transaction decisions including choice of destination, pricing, and passenger choice), any such contracting would imply that the tutors would be employees, and that would significantly increase the platform’s costs (paying for employee benefits are always more expensive than outsourcing to independent service providers).
  3. Increase contract tenure: LocalTutor may increase multi-homing and switching costs by increasing the tenure of the contracting from monthly to annual. Annual contracting will reduce the flexibility that students and tutors have, and might result in reduction in volume.
  4. The next options for LocalTutor are to work at the two restraining assumptions we made at the beginning – penetration and perpetual matching. LocalTutor might want to add in more and more students and tutors and expand the market, providing unique and differentiated services like art & craft classes, preparation for science Olympiads, or other competitive tests. LocalTutor might also communicate the value of teaming of student-tutor pairs in its success stories, in a bid to dis-incentivise switching and multi-homing.

To predate or not to predate is not the question

Given the differences between pipeline and platform businesses new entrants seeking to mobilize network effects have very little option but to resort to predatory pricing. The choice is not if, but how. And as an incumbent, should you be prepared for a new entrant who would resort to predatory pricing? Surely, yes! And how? By being ready to expand the market and increasing switching and multi-homing costs. Unlike in the tutoring business that is inherently geographically constrained, a lot of businesses could span across markets. Even tutoring could leverage technology to reach a global audience.

Just one comforting thought, predatory pricing as a strategy to eliminate competition is inefficient in the long run. The new entrant might adopt predatory pricing to eliminate competition in the short run, but the act of predatory pricing breaks down most barriers to entry, and sends signals to others that there is a market that is easy to enter. It might attract a more highly capitalised competitor to enter the market with the same strategies … making the market a ‘contestable market’. And no one wants to make a fortune in a contestable market, right? More on competing in contestable markets, subsequently.

Cheers

© 2017. R Srinivasan

 

App-in-app?

I recently got an email from my airline app that I could book my car ride within the same app. It was a way of providing end-to-end services. Much like the home pickup and drop service provided for business class customers by the Emirates. What are the implications of these for the customer, the airline, and the cab-hailing firm? Let’s explore.

It is an app-redirect

First, read the terms of how it works in the case of Jet Airways and Uber here. The substantive part of the T&C is hidden in the paragraphs quoted below:

“PLEASE NOTE, YOU ARE MAKING THE PAYMENT TO UBER DIRECTLY. JET AIRWAYS IS NOT RESPONSIBLE / INVOLVED IN THIS FULFILMENT PROCESS. JET AIRWAYS WILL NOT BE LIABLE AND/OR RESPONSIBLE FOR REFUNDS, DELAYS, REJECTIONS, PAYMENT AND FULFILLMENT OR OTHERWISE OF THE SERVICES OR IN RESPECT OF ANY DISPUTES IN RELATION THERETO, IN ANY MANNER WHATSOEVER.” (emphasis original)

Then, what is the value of this app-in-app integration?

Customer perspective

For the customer, it has the potential to work as a seamless end-to-end service. I imagine a future, where you would find a partner using Tinder or TrulyMadly, plan your evening to a game/ movie using BookMyShow, find a restaurant & book your table using Zomato, and take Uber whenever you are ready to move on, or better still, have an Ola Rentals car waiting for you through the evening. All in one app. Wouldn’t you love it, if all of it were integrated in one App? Just imagine the convenience if your restaurant-finder knew that you are in a particular concert at a specific place and you are likely to head out for dinner at a particular time. This specific knowledge could immensely help your restaurant-finder app to customize the experience for you – for instance, it could not only provide you those restaurant options that are open late in the evening after the concert was over, in a location that is close to the venue; it could possibly alert the restaurant that you were arriving in 15 minutes, based on your Uber location. And through the evening, post your pictures on Instagram and SnapChat, check-in to all those locations in Facebook, and Tweet the experience live.

Yes, you would leave a perfect trail for the entire evening in a single place, and if you were to be involved in an investigation, it would be so easy for the officer to trace you! No need for Sherlock Holmes and Watson here – the integrator app would take care of all the snooping for you!

Convenience or scary? What are the safeguards related to such data sharing across different entities? How will the data be regulated?

The Integrator perspective

Why would a Jet Airways provide an Uber link inside its App? Surely cab-hailing and air travel are complementary services. Plus, Jet Airways believes that its customers would find it convenient to book an Uber ride from within the Jet Airways app, as they trust the app to provide Uber with all the relevant details – like the estimated landing/ boarding time of the flight, drop/ pickup addresses, etc. Jet Airways also needs to believe that its customers would rather choose an Uber cab, rather than its competitor OLA Cabs, or any other airport taxi service. The brands should have compatible positioning. Given that Jet Airways is a full service carrier, and differentiates based on its service quality, Uber might be a good fit. But the same might not hold good for a low-cost/ regional carrier like TruJet connecting cities like Tirupati, where Uber does not operate.

Does integrating complementary services affect customer satisfaction, brand loyalty, customer switching costs, and/or multi-homing costs? In contexts where these services and brands are compatible, and there is a convenience involved in sharing of data between these services, there is likely to be some value added. Like airlines and hotels (hotels would like to know your travel schedule); currency exchanges and international travel (the currency exchange would love to know which countries you are visiting); or international mobile services. If there was no data to be shared between the complementary services, the user would rather have them unbundled. Think travel and stock brokerage.

That said, platforms find innovative complementarities. For instance, airlines (primarily the full-service carriers) have launched co-branded credit cards. In a recent visit to Chennai, there were more American Express staff at the Jet Airways lounge than the airline or lounge staff! And they were obviously signing up customers. What are the complementarities between credit cards and air travel, apart from paying from that card? A lot of business travellers have their business travel desks do the payments; consultants have their clients booking the tickets; and even for individuals and entrepreneurs, the credit card market is so fragmented that everyone holds multiple cards. And the payment gateways accept all possible payment options, including “paying cash at the airport counter”. They why co-brand credit cards – sharing of reward points/ airline miles. Either customers do not earn sufficient airline miles and using these co-branded credit cards help them earn more miles and retain/ upgrade their airline status (remember the 2009 movie, Up in the Air?); or they do not earn enough reward points in using their credit cards that they can redeem their airline miles as credit card reward points. Either ways, each one is covering up for the other.

In this covering up, or more diplomatically consolidation of rewards, the partners increase customer switching and multi-homing costs. Surely, redeemable airline miles might be more valuable to a frequent traveller than credit card reward points that have limited redemption/ cash back opportunities. But for loyalty to increase, it is imperative that both brands stand on their own – providing compatible services.

Mother of all apps

All this looks futuristic to you? A lot of you have been using an ubiquitous desktop app known as the browser for a long time, which has been doing exactly this! In a subtle form, though. However, there are firms that own multiple such apps, and they use a single sign-on – like all of Google services. Plus, even third-party sites like Quora allow for using your Google credentials to sign-in. The trade-offs are not always explicitly specified – it is always the case of caveat emptor – consumer beware.

Quora homepage

So, the next time you experience some cross-marketing across platforms/ apps, think what data might be shared across both the apps; and if you would really value the integration.

Cheers!

(c) 2017. R Srinivasan

 

Surge pricing for food delivery: when not to use surge pricing?

This post comes to you from Friedrich Alexander Universitat Erlangen-Nuremberg, where I am visiting for the past one week. I have been teaching a course on Platform Strategies here for the past four years. While in Nuremberg, the question has always been about food, how does a vegetarian, teetotaler survive in Franconia, Bavaria, Germany? To be fair, I have had great vegetarian food here in Nuremberg over the past so many years, and this year has been exceptional – we (my teaching assistant and I) have found great Indian restaurants, that I have had an Indian vegetarian meal for dinner every day of my stay here (except one night of Italian food). Thank you, Nuremberg.

Coming back to food, I was intrigued when I read in the Uber company blog (read it here) that Uber Eats (Uber’s food delivery service) would begin charging customers surge pricing. Much like the way they charge for their  ride-hailing services. I began looking for when and how surge pricing can work. I believe it is a function of customer willingness to pay in part, but most importantly, the platform’s ability to scale up and down service levels at will on the other part.

Economics of the surge

A market is made up of demand, supply, pricing and the norms around exchange. For a market to function, the norms of exchange should be fair and acceptable to the transacting parties. Some markets are defined by the actions of intermediaries who set the norms of exchange, like a stock exchange, a municipal council, or a platform like Uber. In most cases, these intermediaries are third parties in the true sense of the word, “third”, meaning independent of the transacting parties. And in a ‘efficient market’, the intermediary sets the boundaries of behaviours of the transacting parties, and let them transact with little or no involvement. However, in platforms like Uber, the intermediary takes a much larger role, say in pricing. It not only decides the prices of the rides (for both riders and drivers), it also uses pricing as a tool to modify demand and supply conditions. Surge pricing is used as a mechanism to increase supply of cars (by motivating more driver partners to join the system at that point of time), and decrease the demand for cars (by getting riders to either postpone their rides to off-peak times or move away from Uber to other modes of transport, like bus or train). There is enough that has been written about surge pricing, including in this very blog, previously.

Surge pricing in food delivery

Alison Griswold wrote in the Quartz online magazine about why surge pricing for food delivery by Uber Eats is a bad idea (read his article here). She definitely writes wonderful stories about the sharing economy. She argues that once Uber Eats introduces surge pricing, customers would shift away from Uber, and move on to other services, may be even Amazon (with its Prime services). Given that food delivery services do not have high multi-homing costs (customers can simultaneously affiliate with multiple service providers at the same time), and some services may cater to special preferences like a specific cuisine, customers might surely switch in terms of choosing their delivery partner, their restaurant choice, or both. But that can be overcome by just simple speed and other aspects of service quality.

However, her main argument is that the economics of surge pricing might work for increasing more delivery partners to join the system in times of peak demand, but might not get the restaurants to produce more food. She avers that increasing the supply of food available for delivery is not the same as increasing the supply of delivery partners. Fair point. But, don’t restaurants anyway plan for increase in food supply during lunch and dinner times? Don’t they build in some buffer of raw material, ingredients, and/ or semi-processed food before they toss them on the stove? Aren’t there some limits to which they can extend?

Where does surge pricing not work?

Surge pricing works in markets where the intermediaries can, at least at the margin, increase the supply of goods and services and/ or decrease the demand for goods and services. In the case of ride-hailing services, surge pricing can shift people away from ride-hailing to use buses/ trains or just walk. Surge pricing works best when there is idle capacity not available to the users – when the driver partners are present but are themselves taking a break (not logged in) and are not available to take rides. Surge pricing motivates these ‘idle’ capacity to join the market, and restores the balance. In summary, surge pricing works when the demand side has ‘substitutes’ and the supply side has ‘excess capacity’.

If either of these conditions are not met, surge pricing might not work. Take an instance when a cricket/ football game or a concert ends in the middle of the night, and there are no public transportation options. Any amount of surge pricing is unlikely to reduce the demand for cars. Or try surge pricing of rail tickets in Indian trains. Any amount of surge pricing is not going to motivate the rail authorities to increase capacity to balance the market (I am not even convinced it should be called surge pricing – it is just differential pricing of different tickets, depending on whether I am the first person booking the seat or the last). In both of these conditions, differential pricing might be grudgingly accepted by the transacting parties, without any impact on the demand-supply mismatches. Take for example, Kayani Bakery in Pune, India, where by noon they are sold out! Surely, no amount of surge pricing is motivating these businessmen to increase supply. In fact, the scarcity increases the demand for these biscuits.

What are the welfare effects of surge pricing?

Scarcity principle tells us that when supply is far less than demand, prices will rise to ensure that supply matches demand. In an ideal world, both supply will increase and demand will fall. However, in contexts where supply is limited or inelastic, it will be demand that has to come down. In the case of essential goods and services (inelastic demand), prices continue to rise to point where only the wealthy could afford it. This is precisely the reason why governments indulge in market intervention mechanisms. For those interested in how commodity prices can bring down governments, read this!

The lesson for platform business firms: engage in surge pricing only when you can work towards increasing supply, or your demand side has (at least imperfect) substitutes.

(c) 2016. Srinivasan R

Will you look for jobs in Facebook?

It has been a wonderful week so far with my lectures on Platform Business Models at the University of Rome Tor Vergata over the past two days. In one of the discussions, there was a discussion about network mobilisation, and a perceptive participant quipped about how successful Facebook can be in a variety of businesses. I have been maintaining that Facebook and LinkedIn are in independent markets, with their own unique needs, and therefore would never end up competing. However, this discussion on what Facebook can do with the big, small and thick data it has about users – ads, shopping, or even jobs set me thinking.

Winner takes all markets

One of the most common discussions on platform and networked businesses is the prevalence of monopolies, in what we call as “winner-takes-all” markets. There are three conditions for these markets to satisfy to qualify as “winner-takes-all” markets – multi-homing costs should be very high; network effects should be strong and positive; and users usually do not have any special preferences (read more about it here). Social networking (with peers, friends, and family) is a winner-takes-all business by all counts – it is difficult to affiliate yourself with multiple platforms; network effects are strong and positive; and Facebook is used for pretty much everything – no special preferences.

Professional networking space, on the other hand, would have different economics. Multi-homing costs are sure high, but not so high. Especially when people have multiple identities … for instance a CEO by the day and a triathlon by the evening; or a professor of law and counsel at the same time. And they could possibly have separate professional networks, right for each of their interests, right. On top of this, online media provide us with our own masks, that enable us to insulate the two worlds when we choose to or integrate when it suits us. A sort of maskenfrieheit, a German word that translates to “masks provided to us by the power of anonymity”. Most of us surely live in multiple worlds, leveraging our own maskenfreiheits. Network effects are sure strong and positive, and in addition to social networking, professional networking business also has a significant extent of cross-side network effects (from potential employers and followers). There are special preferences in professional networking – there are those wo write for others to follow; some others just read and follow and minimally engage (a occasional like here and a share there); and there are few INfluencers (as LinkedIn calls them). So, it makes logical sense that a professional networking business is not a winner-takes-all business, and should be prepared to be attacked by a variety of competitors.

LinkedIn, for its part has done it bit, I would say. It has significantly expanded its reach to college students; allowed for writing (competing with blogs); jobs (competing with focused recruitment sites); shares, likes, and comments (competing with social networking, including micro-blogging). And its merger with Microsoft recently would hopefully provide it more teeth to bite in.

Facebook enters the jobs market

But, how does LinkedIn compete when the ubiquitous Facebook decides to enter the jobs market? I recently read this report on TechCrunch (read it here) on how Facebook is entering the jobs market. With its size of members’ network at more than thrice that of LinkedIn, Facebook can unearth more and more passive job seekers. Those of you who are not actively seeking a job, but would be interested in testing something out, if is offers great roles, salaries, titles, locations, or just more fun that your current role. In fact, the value proposition of LinkedIn was just that – one keeps building a stack of endorsements and a network that will then actively seek you out, rather than the job seeker reaching out. Facebook seems to have imitated just that – its profile tags is much the same as LinkedIn endorsements. Everyone sees the similarity … read the Fortune Business report of July 2015 here.

Is the professional networking space contestable?

Firms competing across business lines can also be explained using the theory of contestable markets. The simplest definition and explanation of contestable markets I could find online is on this page. These markets are characterised by low barriers to entry (like no economies of scale) and low barriers to exit (like no sunk costs), and therefore allow for new entrants to adopt a hit and run strategy. Incumbents typically protect their turf using asymmetric information (some specific information/ competence) that the new entrants do not possess. If we were to look at professional networking space as a contestable market, then LinkedIn had it all covered as an incumbent. Facebook anyway had a variety of small and medium businesses maintaining pages to connect with its customers; and all it had to do was to extend the same feature to job applicants connecting with the firms. Much like how a firm would announce a new product or a discount offer, it could advertise jobs on its Facebook page. Just that Facebook is trying to overcome the asymmetric information bit with its Profile Tags feature to quickly imitate LinkedIn’s endorsements (it is not available in all countries, yet). Without that Facebook would not be able to customise the feed to its readers – you would get only “relevant” job offers on your Facebook timeline, now that it would have your Profile Tags.

Facebook jobs, anyone?

So, would you apply for jobs using Facebook? I for one know a lot of active seekers and college students invest in building their LinkedIn profiles, rather than “wasting time” on Facebook. Facebook is for casual chit-chat with friends and family, sharing selfies, religious views, political statements, and even late-night party stories. Not the place where I would imagine a lot of people would apply for jobs. Will you let your maskenfreiheit down?

But hang on, what about those who do not have a LinkedIn profile? What about those who are logged on to Facebook for ever on their smart phones? What about those who use Facebook to gather information about jobs and then apply for the same using traditional job sites, just email, or through their LinkedIn profiles? Small and Medium businesses might be able to attract a lot of undifferentiated talent (I’m not talking about blue collared workers only) through Facebook, if this succeeds. And what do dedicated job sites like Monster.com do?

Facebook surely has big data, small data (or thick data) and even the right data (after my posts of the last two weeks, this HBR post on right data appeared online!). Exciting times ahead.

Cheers.

(c) 2016. Srinivasan R.

Regulating Platforms

Over the past few months, there have been a lot of disputes between platform businesses, governments, and a lot of these have gone to courts as well. Last Friday (26 August 2016) issue of the Mint newspaper carried an opinion piece titled “the tricky business of regulating disruptors” (read it here). The editorial while labeling almost all platform businesses as disruptors, just stopped short of calling all of them disruptors. In this blog post, I dig deep into the issue of if and how platform businesses need to be regulated with respect to consumer protection, without impeding innovation and thence providing fair business opportunities to businesses (and returns to investors).

Defining the industry boundaries

One of the key determinants of “competitive” behavior is the definition of the relevant industry. What is competitive and what is anti-competitive can depend on how narrow or broad you cast your net while defining the industry. For instance, the Mint editorial explains in detail how in a 1953 verdict on DuPont’s monopoly on the cellophane as a result of “result, business skill, and competitive activity”, despite having over 75% market share in the cellophane market, because the courts defined the “relevant” market as flexible packaging material, and not cellophane, the product. However, in most cases against platform businesses like Uber, the competition commissions and other regulators have defined the market as app-based taxi services, and therefore looked at the market being usurped by monopolies (Didi-Uber combine in China) or a duopoly comprising of Uber and a local operator (like Grab in SE Asia, OLA in India, Lyft in the USA).

Is Uber a competitor or substitute to Taxi?

In a detailed response to Prof. Aswath Damodaran’s 2014 article on Uber’s valuation (read it here), Bill Gurley (a series A investor and board member of Uber) defined three things (read Bill Gurley’s blog post here).

  1. He argues that Uber has since transformed the industry so much that one’s market size estimates based on current taxi market sizes is flawed. In other words, Uber was providing customers with far more value and a very different set of value propositions than a traditional taxi service – quick discovery, easy payment, predictability of service, quality (dual rating of riders and drivers), and trust/ safety. He talked about how Uber’s customers are using it to transport young adults/ children or older parents in the “comfort and safety” of an Uber, rather than a taxi.
  2. He argued that given the economies of scale that arose due to the positive cross-side network effects, more and more drivers and riders adopted Uber, and Uber expanded to more and more geographies, and the prices fell. And the price elasticity contributed to more demand and therefore more network effects. The economics of Uber (and therefore other ride-hailing app-based services) are very different from the city Taxi services.
  3. Uber is not a taxi alternative – it is a car-ownership (or a car-rental) alternative. When the liquidity (availability + density) of Uber vehicles is so high in every geography you want to travel to, you would rather not rent/ buy a car, but use Uber. The convenience and reduced cost of Uber as an alternative to ownership is something that he substantiates with data and analysis.

In other words, Uber was indeed a disruptor, and therefore was entitled to be treated as a separate industry. It is not a competitor to the for-hire taxi, it is an alternative; much the same way Kodak was bankrupted by digital photography (and not by competitors like Fuji).

Creative destruction and Schumpeterian waves of technology innovation

The Mint editorial called for Honorable Judges to not set taxi fares, simply because these disruptors would transform the industry through their technology innovation, and any restraining regulation would hinder these Schumpeterian waves. It is therefore an indirect call for letting these disruptors alone, let the waves of Schumpeterian technology innovation hit the markets, before we arrive at a stability of sorts. Regulation can wait.

Can regulation wait, and allow for a disruptor, in the excuse that the market is a “winner-takes-all” market monopolize the market? The popular arguments against monopolies is that of consumer protection, and that when monopolies rule, consumers suffer – prices rise, service levels fall, and there may be no alternatives. This is exactly the case for another wave of creative destruction.

My primary thesis is that when such disruptions happen on the basis of network effects, leading to economies and scale, and the disruption is based on parameters like improved customer service, lower prices, and transparent/ fair transactions (trust/ safety and the like), monopolies are not necessarily bad. When such monopolies emerge and the customer experiences deteriorate, as dictated by traditional industrial economics theory, the market will be ripe for another wave of Schumpeterian technology innovation. The waves of market entry in the Indian airlines market is testimony to these (1990s – privatization and shake-up leaving two state-owned and two private competitors; 2000s – entry of low-cost carriers leading to the demise/ consolidation of all stuck-in-the middle competitors; 2010s – entry and strengthening of regional airlines, is it?) waves of creative destruction.

Yes, there is space for other competitors, but not so much for Uber replicas. The market is indeed a winner-takes-all market (as I have argued in the past), and therefore there is just enough room for small, losing replicators. Look around the markets for Uber competitors, you do not find any market fragmented. While differentiation and creating niches is the prescription for firms competing with Uber, I request the regulators to begin treating such platform businesses as an independent market and let the inefficient product-markets fail, if required. No one cried when the offline ticket counters of Indian Railways are declining sales, thanks to the volumes garnered by IRCTC (some claim that this is the world’s largest ecommerce platform, is that true?). No one complains about bookmyshow.com garnering huge market shares in the app-based movie seat booking market, claiming that the livelihoods of the ticket clerks are under threat. Why cry about Uber, or for that matter, OLA, Grab, or Lyft?

There is already sufficient discrimination against these disruptors. In a recent visit to San Francisco, I made an extra effort (okay, walked down a flight of escalators) to click a picture at the SFO airport that read, “app-based taxis to pick-up from departures level”. Honorable Judges, please leave them alone, enjoy your ride/ movies/ every other service, contribute to the economies of scale, and let the market be disrupted.

Cheers.